Predictive speed map generation and control system

ABSTRACT

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestrymachines, construction machines and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugar cane harvesters, cotton harvesters, self-propelledforage harvesters, and windrowers. Some harvester can also be fittedwith different types of heads to harvest different types of crops.

A variety of different conditions in fields have a number of deleteriouseffects on the harvesting operation. Therefore, an operator may attemptto modify control of the harvester, upon encountering such conditionsduring the harvesting operation.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

One or more information maps are obtained by an agricultural workmachine. The one or more information maps map one or more agriculturalcharacteristic values at different geographic locations of a field. Anin-situ sensor on the agricultural work machine senses an agriculturalcharacteristic as the agricultural work machine moves through the field.A predictive map generator generates a predictive map that predicts apredictive agricultural characteristic at different locations in thefield based on a relationship between the values in the one or moreinformation maps and the agricultural characteristic sensed by thein-situ sensor. The predictive map can be output and used in automatedmachine control.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to examples that solveany or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of oneexample of a combine harvester.

FIG. 2 is a block diagram showing some portions of an agriculturalharvester in more detail, according to some examples of the presentdisclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3 ) show a flowdiagram illustrating an example of operation of an agriculturalharvester in generating a map.

FIG. 4 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 5 is a flow diagram showing an example of operation of anagricultural harvester in receiving an information map, detecting aspeed characteristic, and generating a functional predictive speed mapfor use in controlling the agricultural harvester during a harvestingoperation.

FIG. 6 is a block diagram showing one example of an agriculturalharvester in communication with a remote server environment.

FIGS. 7-9 show examples of mobile devices that can be used in anagricultural harvester.

FIG. 10 is a block diagram showing one example of a computingenvironment that can be used in an agricultural harvester and thearchitectures illustrated in previous figures.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, and/or steps described with respect toone example may be combined with the features, components, and/or stepsdescribed with respect to other examples of the present disclosure.

The present description relates to using in-situ data taken concurrentlywith an agricultural operation, in combination with prior data, togenerate a predictive map and, more particularly, a predictive speedmap. In some examples, the predictive speed map can be used to controlan agricultural work machine, such as an agricultural harvester. Asdiscussed above, it may improve the performance of the agriculturalharvester to control the speed of the agricultural harvester when theagricultural harvester engages different conditions in the field. Forinstance, if the crops have reached maturity, the weeds may still begreen, thus increasing the moisture content of the biomass that isencountered by the agricultural harvester. This problem may beexacerbated when the weed patches are wet (such as shortly after arainfall or when weed patches contain dew) and before the weeds have hada chance to dry. Thus, when the agricultural harvester encounters anarea of increased biomass, the operator may slow the speed of theagricultural harvester to maintain a constant feed rate of materialthrough the agricultural harvester. Maintaining a constant feed rate maymaintain the performance of the agricultural harvester.

Performance of an agricultural harvester may be deleteriously affectedbased on a number of different criteria. Such different criteria mayinclude changes in biomass, crop state, topography, soil properties, andseeding characteristics, or other conditions. Therefore, it may also beuseful to control the speed of the agricultural harvester based on otherconditions that may be present in the field. For example, theperformance of the agricultural harvester may be maintained at anacceptable level by controlling the speed of the agricultural harvesterbased on the biomass encountered by the agricultural harvester, the cropstate of the crop being harvested, the topography of the field beingharvested, soil properties of soil in the field being harvested, seedingcharacteristics in the field being harvested, yield in the field beingharvested, or other conditions that are present in the field.

Some current systems provide vegetative index maps. A vegetative indexmap illustratively maps vegetative index values (which may be indicativeof vegetative growth) across different geographic locations in a fieldof interest. One example of a vegetative index includes a normalizeddifference vegetation index (NDVI). There are many other vegetativeindices that are within the scope of the present disclosure. In someexamples, a vegetative index may be derived from sensor readings of oneor more bands of electromagnetic radiation reflected by the plants.Without limitations, these bands may be in the microwave, infrared,visible or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can be used to identify the presence and locationof vegetation. In some examples, these maps enable vegetation to beidentified and georeferenced in the presence of bare soil, crop residue,or other plants, including crop or other weeds.

In some examples, a biomass map is provided. A biomass mapillustratively maps a measure of biomass in the field being harvested atdifferent locations in the field. A biomass map may be generated fromvegetative index values, from historically measured or estimated biomasslevels, from images or other sensor readings taken during a previousoperation in the field, or in other ways. In some examples, biomass maybe adjusted by a factor representing a portion of total biomass passingthrough the agricultural harvester. For corn, this factor is typicallyaround 50%. In some examples, this factor may vary based on cropmoisture. In some examples, the factor may represent a portion of weedmaterial or weed seeds. In some examples, the factor may represent aportion of one crop in an intercrop mix.

In some examples, a crop state map is provided. Crop state may definewhether the crop is down, standing, partially down, the orientation ofdown or partially down crop relative to the ground surface or to acompass direction, and other things. A crop state map illustrativelymaps the crop state in the field being harvested at different locationsin the field. A crop state map may be generated from aerial or otherimages of the field, from images or other sensor readings taken during aprior operation in the field or in other ways prior to harvesting.

In some examples, a seeding map is provided. A seeding map may mapseeding characteristics such as seed locations, seed variety, or seedpopulation to different locations in the field. The seeding map may begenerated during a past seed planting operation in the field. Theseeding map may be derived from control signals used by a seeder whenplanting seeds or from sensors on the seeder that confirm that a seedwas metered or planted. Seeders may also include geographic positionsensors that geolocate the seed characteristics on the field.

In some examples, a soil property map is provided. A soil property mapillustratively maps a measure of one or more soil properties such assoil type, soil chemical constituents, soil structure, residue coverage,tillage history, or soil moisture in the field being harvested atdifferent locations in the field. A soil properties map may be generatedfrom vegetative index values, from historically measured or estimatedsoil properties, from images or other sensor readings taken during aprevious operation in the field, or in other ways.

In some examples, other information maps are provided. Such informationmaps can include a topographic map of the field being harvested, apredictive yield map for the field being harvested, or other informationmaps.

The present discussion thus proceeds with respect to systems thatreceive an information map of a field or map generated during a prioroperation and also use an in-situ sensor to detect a variable indicativeof one or more of a machine speed and an output from a feed rate controlsystem. The systems generate a model that models a relationship betweenthe information values on the information map and the output values fromthe in-situ sensor. The model is used to generate a functionalpredictive speed map that predicts, for example, an expected machinespeed at different locations in the field. The functional predictivespeed map, generated during the harvesting operation, can be presentedto an operator or other user or used in automatically controlling anagricultural harvester during the harvesting operation, or both.

FIG. 1 is a partial pictorial, partial schematic, illustration of aself-propelled agricultural harvester 100. In the illustrated example,agricultural harvester 100 is a combine harvester. Further, althoughcombine harvesters are provided as examples throughout the presentdisclosure, it will be appreciated that the present description is alsoapplicable to other types of harvesters, such as cotton harvesters,sugarcane harvesters, self-propelled forage harvesters, windrowers, orother agricultural work machines. Consequently, the present disclosureis intended to encompass the various types of harvesters described andis, thus, not limited to combine harvesters. Moreover, the presentdisclosure is directed to other types of work machines, such asagricultural seeders and sprayers, construction equipment, forestryequipment, and turf management equipment where generation of apredictive map may be applicable. Consequently, the present disclosureis intended to encompass these various types of harvesters and otherwork machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1 , agricultural harvester 100 illustratively includesan operator compartment 101, which can have a variety of differentoperator interface mechanisms, for controlling agricultural harvester100. Agricultural harvester 100 includes front-end equipment, such as aheader 102, and a cutter generally indicated at 104. Agriculturalharvester 100 also includes a feeder house 106, a feed accelerator 108,and a thresher generally indicated at 110. The feeder house 106 and thefeed accelerator 108 form part of a material handling subsystem 125.Header 102 is pivotally coupled to a frame 103 of agricultural harvester100 along pivot axis 105. One or more actuators 107 drive movement ofheader 102 about axis 105 in the direction generally indicated by arrow109. Thus, a vertical position of header 102 (the header height) aboveground 111 over which the header 102 travels is controllable byactuating actuator 107. While not shown in FIG. 1 , agriculturalharvester 100 may also include one or more actuators that operate toapply a tilt angle, a roll angle, or both to the header 102 or portionsof header 102. Tilt refers to an angle at which the cutter 104 engagesthe crop. The tilt angle is increased, for example, by controllingheader 102 to point a distal edge 113 of cutter 104 more toward theground. The tilt angle is decreased by controlling header 102 to pointthe distal edge 113 of cutter 104 more away from the ground. The rollangle refers to the orientation of header 102 about the front-to-backlongitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set ofconcaves 114. Further, agricultural harvester 100 also includes aseparator 116. Agricultural harvester 100 also includes a cleaningsubsystem or cleaning shoe (collectively referred to as cleaningsubsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve124. The material handling subsystem 125 also includes discharge beater126, tailings elevator 128, clean grain elevator 130, as well asunloading auger 134 and spout 136. The clean grain elevator moves cleangrain into clean grain tank 132. Agricultural harvester 100 alsoincludes a residue subsystem 138 that can include chopper 140 andspreader 142. Agricultural harvester 100 also includes a propulsionsubsystem that includes an engine that drives ground engaging components144, such as wheels or tracks. In some examples, a combine harvesterwithin the scope of the present disclosure may have more than one of anyof the subsystems mentioned above. In some examples, agriculturalharvester 100 may have left and right cleaning subsystems, separators,etc., which are not shown in FIG. 1 .

In operation, and by way of overview, agricultural harvester 100illustratively moves through a field in the direction indicated by arrow147. As agricultural harvester 100 moves, header 102 (and the associatedreel 164) engages the crop to be harvested and gathers the crop towardcutter 104. An operator of agricultural harvester 100 can be a localhuman operator, a remote human operator, or an automated system. Theoperator of agricultural harvester 100 may determine one or more of aheight setting, a tilt angle setting, or a roll angle setting for header102. For example, the operator inputs a setting or settings to a controlsystem, described in more detail below, that controls actuator 107. Thecontrol system may also receive a setting from the operator forestablishing the tilt angle and roll angle of the header 102 andimplement the inputted settings by controlling associated actuators, notshown, that operate to change the tilt angle and roll angle of theheader 102. The actuator 107 maintains header 102 at a height aboveground 111 based on a height setting and, where applicable, at desiredtilt and roll angles. Each of the height, roll, and tilt settings may beimplemented independently of the others. The control system responds toheader error (e.g., the difference between the height setting andmeasured height of header 104 above ground 111 and, in some examples,tilt angle and roll angle errors) with a responsiveness that isdetermined based on a selected sensitivity level. If the sensitivitylevel is set at a greater level of sensitivity, the control systemresponds to smaller header position errors, and attempts to reduce thedetected errors more quickly than when the sensitivity is at a lowerlevel of sensitivity.

Returning to the description of the operation of agricultural harvester100, after crops are cut by cutter 104, the severed crop material ismoved through a conveyor in feeder house 106 toward feed accelerator108, which accelerates the crop material into thresher 110. The cropmaterial is threshed by rotor 112 rotating the crop against concaves114. The threshed crop material is moved by a separator rotor inseparator 116 where a portion of the residue is moved by dischargebeater 126 toward the residue subsystem 138. The portion of residuetransferred to the residue subsystem 138 is chopped by residue chopper140 and spread on the field by spreader 142. In other configurations,the residue is released from the agricultural harvester 100 in awindrow. In other examples, the residue subsystem 138 can include weedseed eliminators (not shown) such as seed baggers or other seedcollectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some largerpieces of material from the grain, and sieve 124 separates some of finerpieces of material from the clean grain. Clean grain falls to an augerthat moves the grain to an inlet end of clean grain elevator 130, andthe clean grain elevator 130 moves the clean grain upwards, depositingthe clean grain in clean grain tank 132. Residue is removed from thecleaning subsystem 118 by airflow generated by cleaning fan 120.Cleaning fan 120 directs air along an airflow path upwardly through thesieves and chaffers. The airflow carries residue rearwardly inagricultural harvester 100 toward the residue handling subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where thetailings are re-threshed. Alternatively, the tailings also may be passedto a separate re-threshing mechanism by a tailings elevator or anothertransport device where the tailings are re-threshed as well.

FIG. 1 also shows that, in one example, agricultural harvester 100includes machine speed sensor 146, one or more separator loss sensors148, a clean grain camera 150, a forward looking image capture mechanism151, which may be in the form of a stereo or mono camera, and one ormore loss sensors 152 provided in the cleaning subsystem 118.

Machine speed sensor 146 senses the travel speed of agriculturalharvester 100 over the ground. Machine speed sensor 146 may sense thetravel speed of the agricultural harvester 100 by sensing the speed ofrotation of the ground engaging components (such as wheels or tracks), adrive shaft, an axel, or other components. In some instances, the travelspeed may be sensed using a positioning system, such as a globalpositioning system (GPS), a dead reckoning system, a long rangenavigation (LORAN) system, or a wide variety of other systems or sensorsthat provide an indication of travel speed.

Loss sensors 152 illustratively provide an output signal indicative ofthe quantity of grain loss occurring in both the right and left sides ofthe cleaning subsystem 118. In some examples, sensors 152 are strikesensors which count grain strikes per unit of time or per unit ofdistance traveled to provide an indication of the grain loss occurringat the cleaning subsystem 118. The strike sensors for the right and leftsides of the cleaning subsystem 118 may provide individual signals or acombined or aggregated signal. In some examples, sensors 152 may includea single sensor as opposed to separate sensors provided for eachcleaning subsystem 118. Separator loss sensor 148 provides a signalindicative of grain loss in the left and right separators, notseparately shown in FIG. 1 . The separator loss sensors 148 may beassociated with the left and right separators and may provide separategrain loss signals or a combined or aggregate signal. In some instances,sensing grain loss in the separators may also be performed using a widevariety of different types of sensors as well.

Agricultural harvester 100 may also include other sensors andmeasurement mechanisms. For instance, agricultural harvester 100 mayinclude one or more of the following sensors: a header height sensorthat senses a height of header 102 above ground 111; stability sensorsthat sense oscillation or bouncing motion (and amplitude) ofagricultural harvester 100; a residue setting sensor that is configuredto sense whether agricultural harvester 100 is configured to chop theresidue, produce a windrow, etc.; a cleaning shoe fan speed sensor tosense the speed of fan 120; a concave clearance sensor that sensesclearance between the rotor 112 and concaves 114; a threshing rotorspeed sensor that senses a rotor speed of rotor 112; a chaffer clearancesensor that senses the size of openings in chaffer 122; a sieveclearance sensor that senses the size of openings in sieve 124; amaterial other than grain (MOG) moisture sensor that senses a moisturelevel of the MOG passing through agricultural harvester 100; one or moremachine setting sensors configured to sense various configurablesettings of agricultural harvester 100; a machine orientation sensorthat senses the orientation of agricultural harvester 100; and cropproperty sensors that sense a variety of different types of cropproperties, such as crop type, crop moisture, and other crop properties.Crop property sensors may also be configured to sense characteristics ofthe severed crop material as the crop material is being processed byagricultural harvester 100. For example, in some instances, the cropproperty sensors may sense grain quality such as broken grain, MOGlevels; grain constituents such as starches and protein; and grain feedrate as the grain travels through the feeder house 106, clean grainelevator 130, or elsewhere in the agricultural harvester 100. The cropproperty sensors may also sense the feed rate of biomass through feederhouse 106, through the separator 116 or elsewhere in agriculturalharvester 100. The crop property sensors may also sense the feed rate asa mass flow rate of grain through elevator 130 or through other portionsof the agricultural harvester 100 or provide other output signalsindicative of other sensed variables.

Prior to describing how agricultural harvester 100 generates afunctional predictive speed map, and uses the functional predictivespeed map for control, a brief description of some of the items onagricultural harvester 100, and their operation, will first bedescribed. The description of FIGS. 2 and 3 describe receiving a generaltype of information map and combining information from the informationmap with a georeferenced sensor signal generated by an in-situ sensor,where the sensor signal is indicative of a characteristic in the field,such as characteristics of the field itself, crop characteristics ofcrop or grain present in the field, or characteristics of theagricultural harvester. Characteristics of the field may include, butare not limited to, characteristics of a field such as slope, weedintensity, weed type, soil moisture, surface quality; characteristics ofcrop properties such as crop height, crop moisture, crop density, cropstate; characteristics of grain properties such as grain moisture, grainsize, grain test weight; and characteristics of machine operation suchas machine speed, outputs from different controllers, machineperformance such as loss levels, job quality, fuel consumption, andpower utilization. A relationship between the characteristic valuesobtained from in-situ sensor signals or values derived therefrom and theinformation map values is identified, and that relationship is used togenerate a new functional predictive map. A functional predictive mappredicts values at different geographic locations in a field, and one ormore of those values may be used for controlling a machine, such as oneor more subsystems of an agricultural harvester. In some instances, afunctional predictive map can be presented to a user, such as anoperator of an agricultural work machine, which may be an agriculturalharvester. A functional predictive map may be presented to a uservisually, such as via a display, haptically, or audibly. The user mayinteract with the functional predictive map to perform editingoperations and other user interface operations. In some instances, afunctional predictive map can be used for one or more of controlling anagricultural work machine, such as an agricultural harvester,presentation to an operator or other user, and presentation to anoperator or user for interaction by the operator or user.

After the general approach is described with respect to FIGS. 2 and 3 ,a more specific approach for generating a functional predictive speedmap that can be presented to an operator or user, or used to controlagricultural harvester 100, or both is described with respect to FIGS. 4and 5 . Again, while the present discussion proceeds with respect to theagricultural harvester and, particularly, a combine harvester, the scopeof the present disclosure encompasses other types of agriculturalharvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester 100. FIG. 2 shows that agricultural harvester 100illustratively includes one or more processors or servers 201, datastore 202, geographic position sensor 204, communication system 206, andone or more in-situ sensors 208 that sense one or more agriculturalcharacteristics of a field concurrent with a harvesting operation. Anagricultural characteristic can include any characteristic that can havean effect of the harvesting operation. Some examples of agriculturalcharacteristics include characteristics of the harvesting machine, thefield, the plants on the field, and the weather. Other types ofagricultural characteristics are also included. The in-situ sensors 208generate values corresponding to the sensed characteristics. Theagricultural harvester 100 also includes a predictive model orrelationship generator (collectively referred to hereinafter as“predictive model generator 210”), predictive map generator 212, controlzone generator 213, control system 214, one or more controllablesubsystems 216, and an operator interface mechanism 218. Theagricultural harvester 100 can also include a wide variety of otheragricultural harvester functionality 220. The in-situ sensors 208include, for example, on-board sensors 222, remote sensors 224, andother sensors 226 that sense characteristics of a field during thecourse of an agricultural operation. Predictive model generator 210illustratively includes an information variable-to-in-situ variablemodel generator 228, and predictive model generator 210 can includeother items 230. Control system 214 includes communication systemcontroller 229, operator interface controller 231, a settings controller232, path planning controller 234, feed rate controller 236, header andreel controller 238, draper belt controller 240, deck plate positioncontroller 242, residue system controller 244, machine cleaningcontroller 245, zone controller 247, and system 214 can include otheritems 246. Controllable subsystems 216 include machine and headeractuators 248, propulsion subsystem 250, steering subsystem 252, residuesubsystem 138, machine cleaning subsystem 254, and subsystems 216 caninclude a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receiveinformation map 258. As described below, the information map 258includes, for example, a vegetative index map, a biomass map, a cropstate map, a topographic map, a soil property map, a seeding map, or amap from a prior operation. However, information map 258 may alsoencompass other types of data that were obtained prior to a harvestingoperation or a map from a prior operation. FIG. 2 also shows that anoperator 260 may operate the agricultural harvester 100. The operator260 interacts with operator interface mechanisms 218. In some examples,operator interface mechanisms 218 may include joysticks, levers, asteering wheel, linkages, pedals, buttons, dials, keypads, useractuatable elements (such as icons, buttons, etc.) on a user interfacedisplay device, a microphone and speaker (where speech recognition andspeech synthesis are provided), among a wide variety of other types ofcontrol devices. Where a touch sensitive display system is provided,operator 260 may interact with operator interface mechanisms 218 usingtouch gestures. These examples described above are provided asillustrative examples and are not intended to limit the scope of thepresent disclosure. Consequently, other types of operator interfacemechanisms 218 may be used and are within the scope of the presentdisclosure.

Information map 258 may be downloaded onto agricultural harvester 100and stored in data store 202, using communication system 206 or in otherways. In some examples, communication system 206 may be a cellularcommunication system, a system for communicating over a wide areanetwork or a local area network, a system for communicating over a nearfield communication network, or a communication system configured tocommunicate over any of a variety of other networks or combinations ofnetworks. Communication system 206 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card or both.

Geographic position sensor 204 illustratively senses or detects thegeographic position or location of agricultural harvester 100.Geographic position sensor 204 can include, but is not limited to, aglobal navigation satellite system (GNSS) receiver that receives signalsfrom a GNSS satellite transmitter. Geographic position sensor 204 canalso include a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensor 204 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

In-situ sensors 208 may be any of the sensors described above withrespect to FIG. 1 . In-situ sensors 208 include on-board sensors 222that are mounted on-board agricultural harvester 100. Such sensors mayinclude, for instance, any of the sensors discussed above with respectto FIG. 1 , a perception sensor (e.g., a forward looking mono or stereocamera system and image processing system), image sensors that areinternal to agricultural harvester 100 (such as the clean grain cameraor cameras mounted to identify material that is exiting agriculturalharvester 100 through the residue subsystem or from the cleaningsubsystem). The in-situ sensors 208 also include remote in-situ sensors224 that capture in-situ information. In-situ data include data takenfrom a sensor on-board the harvester or taken by any sensor where thedata are detected during the harvesting operation.

Predictive model generator 210 generates a model that is indicative of arelationship between the values sensed by the in-situ sensor 208 and ametric mapped to the field by the information map 258. For example, ifthe information map 258 maps a vegetative index value to differentlocations in the field, and the in-situ sensor 208 is sensing a valueindicative of machine speed, then information variable-to-in-situvariable model generator 228 generates a predictive speed model thatmodels the relationship between the vegetative index value and themachine speed value. The predictive speed model can also be generatedbased on vegetative index values from the information map 258 andmultiple in-situ data values generated by in-situ sensors 208. Then,predictive map generator 212 uses the predictive speed model generatedby predictive model generator 210 to generate a functional predictivespeed map that predicts the expected machine speed sensed by the in-situsensors 208 at different locations in the field based upon theinformation map 258.

In some examples, the type of values in the functional predictive map263 may be the same as the in-situ data type sensed by the in-situsensors 208. In some instances, the type of values in the functionalpredictive map 263 may have different units from the data sensed by thein-situ sensors 208. In some examples, the type of values in thefunctional predictive map 263 may be different from the data type sensedby the in-situ sensors 208 but have a relationship to the type of datatype sensed by the in-situ sensors 208. For example, in some examples,the data type sensed by the in-situ sensors 208 may be indicative of thetype of values in the functional predictive map 263. In some examples,the type of data in the functional predictive map 263 may be differentthan the data type in the information map 258. In some instances, thetype of data in the functional predictive map 263 may have differentunits from the data in the information map 258. In some examples, thetype of data in the functional predictive map 263 may be different fromthe data type in the information map 258 but has a relationship to thedata type in the information map 258. For example, in some examples, thedata type in the information map 258 may be indicative of the type ofdata in the functional predictive map 263. In some examples, the type ofdata in the functional predictive map 263 is different than one of, orboth of the in-situ data type sensed by the in-situ sensors 208 and thedata type in the information map 258. In some examples, the type of datain the functional predictive map 263 is the same as one of, or both of,of the in-situ data type sensed by the in-situ sensors 208 and the datatype in information map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of the in-situ datatype sensed by the in-situ sensors 208 or the data type in theinformation map 258, and different than the other.

Continuing with the preceding example, in which information map 258 is avegetative index map and in-situ sensor 208 senses a value indicative ofmachine speed, predictive map generator 212 can use the vegetative indexvalues in information map 258 and the model generated by predictivemodel generator 210 to generate a functional predictive map 263 thatpredicts the expected machine speed at different locations in the field.Predictive map generator 212 thus outputs predictive map 264.

As shown in FIG. 2 , predictive map 264 predicts the value of a sensedcharacteristic (sensed by in-situ sensors 208), or a characteristicrelated to the sensed characteristic, at various locations across thefield based upon an information value in information map 258 at thoselocations and using the predictive model. For example, if predictivemodel generator 210 has generated a predictive model indicative of arelationship between a vegetative index value and machine speed, then,given the vegetative index value at different locations across thefield, predictive map generator 212 generates a predictive map 264 thatpredicts the target machine speed value at different locations acrossthe field. The vegetative index value, obtained from the vegetativeindex map, at those locations and the relationship between vegetativeindex value and machine speed, obtained from the predictive model, areused to generate the predictive map 264.

Some variations in the data types that are mapped in the information map258, the data types sensed by in-situ sensors 208, and the data typespredicted on the predictive map 264 will now be described.

In some examples, the data type in the information map 258 is differentfrom the data type sensed by in-situ sensors 208, yet the data type inthe predictive map 264 is the same as the data type sensed by thein-situ sensors 208. For instance, the information map 258 may be avegetative index map, and the variable sensed by the in-situ sensors 208may be yield. The predictive map 264 may then be a predictive yield mapthat maps predicted yield values to different geographic locations inthe field. In another example, the information map 258 may be avegetative index map, and the variable sensed by the in-situ sensors 208may be crop height. The predictive map 264 may then be a predictive cropheight map that maps predicted crop height values to differentgeographic locations in the field.

Also, in some examples, the data type in the information map 258 isdifferent from the data type sensed by in-situ sensors 208, and the datatype in the predictive map 264 is different from both the data type inthe information map 258 and the data type sensed by the in-situ sensors208. For instance, the information map 258 may be a vegetative indexmap, and the variable sensed by the in-situ sensors 208 may be cropheight. The predictive map 264 may then be a predictive biomass map thatmaps predicted biomass values to different geographic locations in thefield. In another example, the information map 258 may be a vegetativeindex map, and the variable sensed by the in-situ sensors 208 may beyield. The predictive map 264 may then be a predictive speed map thatmaps predicted harvester speed values to different geographic locationsin the field.

In some examples, the information map 258 is from a prior pass throughthe field during a prior operation and the data type is different fromthe data type sensed by in-situ sensors 208, yet the data type in thepredictive map 264 is the same as the data type sensed by the in-situsensors 208. For instance, the information map 258 may be a seedpopulation map generated during planting, and the variable sensed by thein-situ sensors 208 may be stalk size. The predictive map 264 may thenbe a predictive stalk size map that maps predicted stalk size values todifferent geographic locations in the field. In another example, theinformation map 258 may be a seeding hybrid map, and the variable sensedby the in-situ sensors 208 may be crop state such as standing crop ordown crop. The predictive map 264 may then be a predictive crop statemap that maps predicted crop state values to different geographiclocations in the field.

In some examples, the information map 258 is from a prior pass throughthe field during a prior operation and the data type is the same as thedata type sensed by in-situ sensors 208, and the data type in thepredictive map 264 is also the same as the data type sensed by thein-situ sensors 208. For instance, the information map 258 may be ayield map generated during a previous year, and the variable sensed bythe in-situ sensors 208 may be yield. The predictive map 264 may then bea predictive yield map that maps predicted yield values to differentgeographic locations in the field. In such an example, the relativeyield differences in the georeferenced information map 258 from theprior year can be used by predictive model generator 210 to generate apredictive model that models a relationship between the relative yielddifferences on the information map 258 and the yield values sensed byin-situ sensors 208 during the current harvesting operation. Thepredictive model is then used by predictive map generator 210 togenerate a predictive yield map.

In another example, the information map 258 may be a weed intensity mapgenerated during a prior operation, such as from a sprayer, and thevariable sensed by the in-situ sensors 208 may be weed intensity. Thepredictive map 264 may then be a predictive weed intensity map that mapspredicted weed intensity values to different geographic locations in thefield. In such an example, a map of the weed intensities at time ofspraying is geo-referenced recorded and provided to agriculturalharvester 100 as an information map 258 of weed intensity. In-situsensors 208 can detect weed intensity at geographic locations in thefield and predictive model generator 210 may then build a predictivemodel that models a relationship between weed intensity at time ofharvest and weed intensity at time of spraying. This is because thesprayer will have impacted the weed intensity at time of spraying, butweeds may still crop up in similar areas again by harvest. However, theweed areas at harvest are likely to have different intensity based ontiming of the harvest, weather, weed type, among other things.

In some examples, predictive map 264 can be provided to the control zonegenerator 213. Control zone generator 213 groups adjacent portions of anarea into one or more control zones based on data values of predictivemap 264 that are associated with those adjacent portions. A control zonemay include two or more contiguous portions of an area, such as a field,for which a control parameter corresponding to the control zone forcontrolling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 216 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator213 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems216. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 216 or for groups of controllable subsystems 216.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265. Insome examples, multiple crops may be simultaneously present in a fieldif an intercrop production system is implemented. In that case,predictive map generator 212 and control zone generator 213 are able toidentify the location and characteristics of the two or more crops andthen generate predictive map 264 and predictive control zone map 265accordingly.

It will also be appreciated that control zone generator 213 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay be used for controlling or calibrating agricultural harvester 100 orboth. In other examples, the control zones may be presented to theoperator 260 and used to control or calibrate agricultural harvester100, and, in other examples, the control zones may be presented to theoperator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 214, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 229 controlscommunication system 206 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to otheragricultural harvesters that are harvesting in the same field. In someexamples, communication system controller 229 controls the communicationsystem 206 to send the predictive map 264, predictive control zone map265, or both to other remote systems.

Operator interface controller 231 is operable to generate controlsignals to control operator interface mechanisms 218. The operatorinterface controller 231 is also operable to present the predictive map264 or predictive control zone map 265 or other information derived fromor based on the predictive map 264, predictive control zone map 265, orboth to operator 260. Operator 260 may be a local operator or a remoteoperator. As an example, controller 231 generates control signals tocontrol a display mechanism to display one or both of predictive map 264and predictive control zone map 265 for the operator 260. Controller 231may generate operator actuatable mechanisms that are displayed and canbe actuated by the operator to interact with the displayed map. Theoperator can edit the map by, for example, correcting a weed typedisplayed on the map, based on the operator's observation. Settingscontroller 232 can generate control signals to control various settingson the agricultural harvester 100 based upon predictive map 264, thepredictive control zone map 265, or both. For instance, settingscontroller 232 can generate control signals to control machine andheader actuators 248. In response to the generated control signals, themachine and header actuators 248 operate to control, for example, one ormore of the sieve and chaffer settings, concave clearance, rotorsettings, cleaning fan speed settings, header height, headerfunctionality, reel speed, reel position, draper functionality (whereagricultural harvester 100 is coupled to a draper header), corn headerfunctionality, internal distribution control and other actuators 248that affect the other functions of the agricultural harvester 100. Pathplanning controller 234 illustratively generates control signals tocontrol steering subsystem 252 to steer agricultural harvester 100according to a desired path. Path planning controller 234 can control apath planning system to generate a route for agricultural harvester 100and can control propulsion subsystem 250 and steering subsystem 252 tosteer agricultural harvester 100 along that route. Feed rate controller236 may receive a variety of different inputs indicative of a feed rateof material through agricultural harvester 100 and can control varioussubsystems, such as propulsion subsystem 250 and machine actuators 248,to control the feed rate based upon the predictive map 264 or predictivecontrol zone map 265 or both. For instance, as agricultural harvester100 approaches a weed patch having an intensity value above a selectedthreshold, feed rate controller 236 may generate a control signal tocontrol propulsion subsystem 252 to reduce the speed of agriculturalharvester 100 to maintain constant feed rate of biomass through theagricultural harvester 100. Header and reel controller 238 can generatecontrol signals to control a header or a reel or other headerfunctionality. Draper belt controller 240 can generate control signalsto control a draper belt or other draper functionality based upon thepredictive map 264, predictive control zone map 265, or both. Deck plateposition controller 242 can generate control signals to control aposition of a deck plate included on a header based on predictive map264 or predictive control zone map 265 or both, and residue systemcontroller 244 can generate control signals to control a residuesubsystem 138 based upon predictive map 264 or predictive control zonemap 265, or both. Machine cleaning controller 245 can generate controlsignals to control machine cleaning subsystem 254. For instance, basedupon the different types of seeds or weeds passed through agriculturalharvester 100, a particular type of machine cleaning operation or afrequency with which a cleaning operation is performed may becontrolled. Other controllers included on the agricultural harvester 100can control other subsystems based on the predictive map 264 orpredictive control zone map 265 or both as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3 ) show a flowdiagram illustrating one example of the operation of agriculturalharvester 100 in generating a predictive map 264 and predictive controlzone map 265 based upon information map 258.

At 280, agricultural harvester 100 receives information map 258.Examples of information map 258 or receiving information map 258 arediscussed with respect to blocks 281, 282, 284 and 286. As discussedabove, information map 258 maps values of a variable, corresponding to afirst characteristic, to different locations in the field, as indicatedat block 282. As indicated at block 281, receiving the information map258 may involve selecting one or more of a plurality of possibleinformation maps that are available. For instance, one information mapmay be a vegetative index map generated from aerial imagery. Anotherinformation map may be a map generated during a prior pass through thefield which may have been performed by a different machine performing aprevious operation in the field, such as a sprayer or a planting machineor seeding machine or unmanned aerial vehicle (UAV) or other machine.The process by which one or more information maps are selected can bemanual, semi-automated, or automated. The information map 258 is basedon data collected prior to a current harvesting operation. This isindicated by block 284. For instance, the data may be collected based onaerial images taken during a previous year, or earlier in the currentgrowing season, or at other times. The data may be based on datadetected in ways other than using aerial images. For instance,agricultural harvester 100 may be fitted with a sensor, such as aninternal optical sensor, that identifies weed seeds or other types ofmaterial exiting agricultural harvester 100. The weed seed or other datadetected by the sensor during a previous year's harvest may be used asdata used to generate the information map 258. The sensed weed data orother data may be combined with other data to generate the informationmap 258. For example, based upon a magnitude of the weed seeds exitingagricultural harvester 100 at different locations and based upon otherfactors, such as whether the seeds are being spread by a spreader ordropped in a windrow; the weather conditions, such as wind, when theseeds are being dropped or spread; drainage conditions which may moveseeds around in the field; or other information, the location of thoseweed seeds can be predicted so that the information map 258 maps thepredicted seed locations in the field. The data for the information map258 can be transmitted to agricultural harvester 100 using communicationsystem 206 and stored in data store 202. The data for the informationmap 258 can be provided to agricultural harvester 100 usingcommunication system 206 in other ways as well, and this is indicated byblock 286 in the flow diagram of FIG. 3 . In some examples, theinformation map 258 can be received by communication system 206.

Upon commencement of a harvesting operation, in-situ sensors 208generate sensor signals indicative of one or more in-situ data valuesindicative of a characteristic, such as a speed characteristic, asindicated by block 288. Examples of in-situ sensors 288 are discussedwith respect to blocks 222, 290, and 226. As explained above, thein-situ sensors 208 include on-board sensors 222; remote in-situ sensors224, such as UAV-based sensors flown at a time to gather in-situ data,shown in block 290; or other types of in-situ sensors, designated byin-situ sensors 226. In some examples, data from on-board sensors isgeoreferenced using position, heading, or speed data from geographicposition sensor 204.

Predictive model generator 210 controls the informationvariable-to-in-situ variable model generator 228 to generate a modelthat models a relationship between the mapped values contained in theinformation map 258 and the in-situ values sensed by the in-situ sensors208 as indicated by block 292. The characteristics or data typesrepresented by the mapped values in the information map 258 and thein-situ values sensed by the in-situ sensors 208 may be the samecharacteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 isprovided to predictive map generator 212. Predictive map generator 212generates a predictive map 264 that predicts a value of thecharacteristic sensed by the in-situ sensors 208 at different geographiclocations in a field being harvested, or a different characteristic thatis related to the characteristic sensed by the in-situ sensors 208,using the predictive model and the information map 258, as indicated byblock 294.

It should be noted that, in some examples, the information map 258 mayinclude two or more different maps or two or more different map layersof a single map. Each map layer may represent a different data type fromthe data type of another map layer or the map layers may have the samedata type that were obtained at different times. Each map in the two ormore different maps or each layer in the two or more different maplayers of a map maps a different type of variable to the geographiclocations in the field. In such an example, predictive model generator210 generates a predictive model that models the relationship betweenthe in-situ data and each of the different variables mapped by the twoor more different maps or the two or more different map layers.Similarly, the in-situ sensors 208 can include two or more sensors eachsensing a different type of variable. Thus, the predictive modelgenerator 210 generates a predictive model that models the relationshipsbetween each type of variable mapped by the information map 258 and eachtype of variable sensed by the in-situ sensors 208. Predictive mapgenerator 212 can generate a functional predictive map 263 that predictsa value for each sensed characteristic sensed by the in-situ sensors 208(or a characteristic related to the sensed characteristic) at differentlocations in the field being harvested using the predictive model andeach of the maps or map layers in the information map 258.

Predictive map generator 212 configures the predictive map 264 so thatthe predictive map 264 is actionable (or consumable) by control system214. Predictive map generator 212 can provide the predictive map 264 tothe control system 214 or to control zone generator 213 or both. Someexamples of different ways in which the predictive map 264 can beconfigured or output are described with respect to blocks 296, 295, 299and 297. For instance, predictive map generator 212 configurespredictive map 264 so that predictive map 264 includes values that canbe read by control system 214 and used as the basis for generatingcontrol signals for one or more of the different controllable subsystemsof the agricultural harvester 100, as indicated by block 296.

Control zone generator 213 can divide the predictive map 264 intocontrol zones based on the values on the predictive map 264.Contiguously-geolocated values that are within a threshold value of oneanother can be grouped into a control zone. The threshold value can be adefault threshold value, or the threshold value can be set based on anoperator input, based on an input from an automated system, or based onother criteria. A size of the zones may be based on a responsiveness ofthe control system 214, the controllable subsystems 216, based on wearconsiderations, or on other criteria as indicated by block 295.Predictive map generator 212 configures predictive map 264 forpresentation to an operator or other user. Control zone generator 213can configure predictive control zone map 265 for presentation to anoperator or other user. This is indicated by block 299. When presentedto an operator or other user, the presentation of the predictive map 264or predictive control zone map 265 or both may contain one or more ofthe predictive values on the predictive map 264 correlated to geographiclocation, the control zones on predictive control zone map 265correlated to geographic location, and settings values or controlparameters that are used based on the predicted values on map 264 orzones on predictive control zone map 265. The presentation can, inanother example, include more abstracted information or more detailedinformation. The presentation can also include a confidence level thatindicates an accuracy with which the predictive values on predictive map264 or the zones on predictive control zone map 265 conform to measuredvalues that may be measured by sensors on agricultural harvester 100 asagricultural harvester 100 moves through the field. Further whereinformation is presented to more than one location, an authenticationand authorization system can be provided to implement authentication andauthorization processes. For instance, there may be a hierarchy ofindividuals that are authorized to view and change maps and otherpresented information. By way of example, an on-board display device mayshow the maps in near real time locally on the machine, or the maps mayalso be generated at one or more remote locations, or both. In someexamples, each physical display device at each location may beassociated with a person or a user permission level. The user permissionlevel may be used to determine which display markers are visible on thephysical display device and which values the corresponding person maychange. As an example, a local operator of machine 100 may be unable tosee the information corresponding to the predictive map 264 or make anychanges to machine operation. A supervisor, such as a supervisor at aremote location, however, may be able to see the predictive map 264 onthe display but be prevented from making any changes. A manager, who maybe at a separate remote location, may be able to see all of the elementson predictive map 264 and also be able to change the predictive map 264.In some instances, the predictive map 264 accessible and changeable by amanager located remotely may be used in machine control. This is oneexample of an authorization hierarchy that may be implemented. Thepredictive map 264 or predictive control zone map 265 or both can beconfigured in other ways as well, as indicated by block 297.

At block 298, input from geographic position sensor 204 and otherin-situ sensors 208 are received by the control system. Particularly, atblock 300, control system 214 detects an input from the geographicposition sensor 204 identifying a geographic location of agriculturalharvester 100. Block 302 represents receipt by the control system 214 ofsensor inputs indicative of trajectory or heading of agriculturalharvester 100, and block 304 represents receipt by the control system214 of a speed of agricultural harvester 100. Block 306 representsreceipt by the control system 214 of other information from variousin-situ sensors 208.

At block 308, control system 214 generates control signals to controlthe controllable subsystems 216 based on the predictive map 264 orpredictive control zone map 265 or both and the input from thegeographic position sensor 204 and any other in-situ sensors 208. Atblock 310, control system 214 applies the control signals to thecontrollable subsystems. It will be appreciated that the particularcontrol signals that are generated, and the particular controllablesubsystems 216 that are controlled, may vary based upon one or moredifferent things. For example, the control signals that are generatedand the controllable subsystems 216 that are controlled may be based onthe type of predictive map 264 or predictive control zone map 265 orboth that is being used. Similarly, the control signals that aregenerated and the controllable subsystems 216 that are controlled andthe timing of the control signals can be based on various latencies ofcrop flow through the agricultural harvester 100 and the responsivenessof the controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of apredictive speed map can be used to control one or more subsystems 216.For instance, the predictive speed map can include speed valuesgeoreferenced to locations within the field being harvested. The speedvalues from the predictive speed map can be extracted and used tocontrol the propulsion subsystem 250. By controlling the propulsionsubsystem 250, a feed rate of material moving through the agriculturalharvester 100 can be controlled. Similarly, the header height can becontrolled to take in more or less material, and, thus, the headerheight can also be controlled to control feed rate of material throughthe agricultural harvester 100. In other examples, if the predictive map264 maps weed height relative to positions in the field, control of theheader height can be implemented. For example, if the values present inthe predictive weed map indicate one or more areas having weed heightwith a first height amount, then header and reel controller 238 cancontrol the header height so that the header is positioned above thefirst height amount of the weeds within the one or more areas havingweeds at the first height amount when performing the harvestingoperation. Thus, the header and reel controller 238 can be controlledusing georeferenced values present in the predictive weed map toposition the header to a height that is above the predicted heightvalues of weeds obtained from the predictive weed map. Further, theheader height can be changed automatically by the header and reelcontroller 238 as the agricultural harvester 100 proceeds through thefield using georeferenced values obtained from the predictive weed map.The preceding example involving weed height and intensity using apredictive weed map is provided merely as an example. Consequently, awide variety of other control signals can be generated using valuesobtained from a predictive weed map or other type of predictive map tocontrol one or more of the controllable subsystems 216.

At block 312, a determination is made as to whether the harvestingoperation has been completed. If harvesting is not completed, theprocessing advances to block 314 where in-situ sensor data fromgeographic position sensor 204 and in-situ sensors 208 (and perhapsother sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 can alsodetect learning trigger criteria to perform machine learning on one ormore of the predictive map 264, predictive control zone map 265, themodel generated by predictive model generator 210, the zones generatedby control zone generator 213, one or more control algorithmsimplemented by the controllers in the control system 214, and othertriggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 318, 320, 321, 322 and 324. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data are obtained from in-situ sensors 208. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 208 that exceeds a threshold trigger or causes thepredictive model generator 210 to generate a new predictive model thatis used by predictive map generator 212. Thus, as agricultural harvester100 continues a harvesting operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 208 triggers the creationof a new relationship represented by a predictive model generated bypredictive model generator 210. Further, new predictive map 264,predictive control zone map 265, or both can be regenerated using thenew predictive model. Block 318 represents detecting a threshold amountof in-situ sensor data used to trigger creation of a new predictivemodel.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 208 are changing,such as over time or compared to previous values. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in information map 258) arewithin a selected range or is less than a defined amount, or below athreshold value, then a new predictive model is not generated by thepredictive model generator 210. As a result, the predictive mapgenerator 212 does not generate a new predictive map 264, predictivecontrol zone map 265, or both. However, if variations within the in-situsensor data are outside of the selected range, are greater than thedefined amount, or are above the threshold value, for example, then thepredictive model generator 210 generates a new predictive model usingall or a portion of the newly received in-situ sensor data that thepredictive map generator 212 uses to generate a new predictive map 264.At block 320, variations in the in-situ sensor data, such as a magnitudeof an amount by which the data exceeds the selected range or a magnitudeof the variation of the relationship between the in-situ sensor data andthe information in the information map 258, can be used as a trigger tocause generation of a new predictive model and predictive map. Keepingwith the examples described above, the threshold, the range, and thedefined amount can be set to default values; set by an operator or userinteraction through a user interface; set by an automated system; or setin other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 210 switches to a different information map(different from the originally selected information map 258), thenswitching to the different information map may trigger re-learning bypredictive model generator 210, predictive map generator 212, controlzone generator 213, control system 214, or other items. In anotherexample, transitioning of agricultural harvester 100 to a differenttopography or to a different control zone may be used as learningtrigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 orpredictive control zone map 265 or both. The edits can change a value onthe predictive map 264, change a size, shape, position, or existence ofa control zone on predictive control zone map 265, or both. Block 321shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator 260 observes thatautomated control of a controllable subsystem, is not what the operatordesires. In such instances, the operator 260 may provide a manualadjustment to the controllable subsystem reflecting that the operator260 desires the controllable subsystem to operate in a different waythan is being commanded by control system 214. Thus, manual alterationof a setting by the operator 260 can cause one or more of predictivemodel generator 210 to relearn a model, predictive map generator 212 toregenerate map 264, control zone generator 213 to regenerate one or morecontrol zones on predictive control zone map 265, and control system 214to relearn a control algorithm or to perform machine learning on one ormore of the controller components 232 through 246 in control system 214based upon the adjustment by the operator 260, as shown in block 322.Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval, as indicated byblock 326.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 326,then one or more of the predictive model generator 210, predictive mapgenerator 212, control zone generator 213, and control system 214performs machine learning to generate a new predictive model, a newpredictive map, a new control zone, and a new control algorithm,respectively, based upon the learning trigger criteria. The newpredictive model, the new predictive map, and the new control algorithmare generated using any additional data that has been collected sincethe last learning operation was performed. Performing relearning isindicated by block 328.

If the harvesting operation has been completed, operation moves fromblock 312 to block 330 where one or more of the predictive map 264,predictive control zone map 265, and predictive model generated bypredictive model generator 210 are stored. The predictive map 264,predictive control zone map 265, and predictive model may be storedlocally on data store 202 or sent to a remote system using communicationsystem 206 for later use.

It will be noted that while some examples herein describe predictivemodel generator 210 and predictive map generator 212 receiving aninformation map in generating a predictive model and a functionalpredictive map, respectively, in other examples, the predictive modelgenerator 210 and predictive map generator 212 can receive, ingenerating a predictive model and a functional predictive map,respectively other types of maps, including predictive maps, such as afunctional predictive map generated during the harvesting operation.

FIG. 4 is a block diagram of a portion of the agricultural harvester 100shown in FIG. 1 . Particularly, FIG. 4 shows, among other things,examples of the predictive model generator 210 and the predictive mapgenerator 212 in more detail. FIG. 4 also illustrates information flowamong the various components shown. The predictive model generator 210receives information map 258, which may be a vegetative index map 332, apredictive yield map 333, a biomass map 335, a crop state map 337, atopographic map 339, a soil property map 341, a seeding map 343 oranother map 353, as an information map. Predictive model generator 210also receives a geographic location 334, or an indication of ageographic location, from geographic position sensor 204. In-situsensors 208 illustratively include machine speed sensor 146, or a sensor336 that senses an output from feed rate controller 236, as well as aprocessing system 338. The processing system 338 processes sensor datagenerated from machine speed sensor 146 or from sensor 336, or both, togenerate processed data, some examples of which are described below.

In some examples, sensor 336 may be a sensor, that generates a signalindicative of the control outputs from feed rate controller 236. Thecontrol signals may be speed control signals or other control signalsthat are applied to controllable subsystems 216 to control feed rate ofmaterial through agricultural harvester 100. Processing system 338processes the signals obtained via the sensor 336 to generate processeddata 340 identifying the speed of agricultural harvester 100.

In some examples, raw or processed data from in-situ sensor(s) 208 maybe presented to operator 260 via operator interface mechanism 218.Operator 260 may be onboard the agricultural harvester 100 or at aremote location.

The present discussion proceeds with respect to an example in whichin-situ sensor 208 is machine speed sensor 146. It will be appreciatedthat this is just one example, and the sensors mentioned above, as otherexamples of in-situ sensor 208 from which machine speed can be derived,are contemplated herein as well. As shown in FIG. 4 , the examplepredictive model generator 210 includes one or more of a vegetativeindex (VI) value-to-speed model generator 342, a biomass-to-speed modelgenerator 344, topography-to-speed model generator 345, yield-to-speedmodel generator 347, crop state-to-speed model generator 349, soilproperty-to-speed model generator 351 and a seedingcharacteristic-to-speed model generator 346. In other examples, thepredictive model generator 210 may include additional, fewer, ordifferent components than those shown in the example of FIG. 4 .Consequently, in some examples, the predictive model generator 210 mayinclude other items 348 as well, which may include other types ofpredictive model generators to generate other types of models.

Model generator 342 identifies a relationship between machine speeddetected in processed data 340, at a geographic location correspondingto where the processed data 340 were obtained, and a vegetative indexvalue from the vegetative index map 332 corresponding to the samelocation in the field where the speed characteristic was detected. Basedon this relationship established by model generator 342, model generator342 generates a predictive speed model. The predictive speed model isused by speed map generator 352 to predict target machine speeds atdifferent locations in the field based upon the georeferenced vegetativeindex value contained in the vegetative index map 332 at the samelocations in the field.

Model generator 344 identifies a relationship between machine speedrepresented in the processed data 340, at a geographic locationcorresponding to the processed data 340, and the biomass value at thesame geographic location. Again, the biomass value is the georeferencedvalue contained in the biomass map 335. Model generator 344 thengenerates a predictive speed model that is used by speed map generator352 to predict the target machine speed at a location in the field basedupon the biomass value for that location in the field.

Model generator 345 identifies a relationship between machine speedidentified by processed data 340 at a particular location on the fieldand the topographic speed model that is used by speed map generator 352to predict expected machine speed at a particular location in the fieldbased on the topographic characteristic value at that location in thefield.

Model generator 346 identifies a relationship between the machine speedidentified by processed data 340 at a particular location in the fieldand the seeding characteristic value from the seeding characteristic map343 at that same location. Model generator 346 generates a predictivespeed model that is used by speed map generator 352 to predict theexpected machine speed at a particular location in the field based uponthe seeding characteristic value at that location in the field.

Model generator 347 identified a relationship between the machine speedidentified by processed data 340 at a particular location on the fieldand the yield characteristic value from predictive yield map 333 at thatsame location model generator 347 generates a predictive speed modelthat is used by speed map generator 352 to predict the expected machinespeed at a particular location on the field based upon the yieldcharacteristic value at that location in the field.

Model generator 349 identifies a relationship between the machine speedidentified by processed data 340 at a particular location in the fieldand the crop state characteristic value from crop state map 337 at thesame location. Model generator 349 generates a predictive speed modelthat is used by speed map generator 352 to predict the expected machinespeed at a particular location in the field based on the crop statecharacteristic value at that location in the field.

Model generator 351 identifies a relationship between the machine speedidentified by processed data 340 at a particular location in the fieldand the soil property characteristic value from soil property map 341 atthe same location. Model generator 351 generates a predictive speedmodel that is used by speed map generator 352 to predict the expectedmachine speed at a particular location in the field based on the soilproperty characteristic value at that location in the field.

In light of the above, the predictive model generator 210 is operable toproduce a plurality of predictive speed models, such as one or more ofthe predictive speed models generated by model generators 342, 344, 345,346, 347, 349 and 351. In another example, two or more of the predictivespeed models described above may be combined into a single predictivespeed model that predicts expected machine speed based on two or more ofthe vegetative index value, the biomass value, the topography, theyield, the seeding characteristic, the crop state, or the soil property,at different locations in the field. Any of these speed models, orcombinations thereof, are represented collectively by predictive model350 in FIG. 4 .

The predictive model 350 is provided to predictive map generator 212. Inthe example of FIG. 4 , predictive map generator 212 includes a speedmap generator 352. In other examples, the predictive map generator 212may include additional, fewer, or different map generators. Thus, insome examples, the predictive map generator 212 may include other items358 which may include other types of map generators to generate speedmaps. Speed map generator 352 receives the predictive model 350, whichpredicts target machine speed based upon a value from one or moreinformation maps 258, along with the one or more information maps 258,and generates a predictive map that predicts the target machine speed atdifferent locations in the field.

Predictive map generator 212 outputs one or more functional predictivespeed maps 360 that are predictive of expected machine speed. Thefunctional predictive speed map 360 predicts the expected machine speedat different locations in a field. The functional predictive speed maps360 may be provided to control zone generator 213, control system 214,or both. Control zone generator 213 generates control zones andincorporates those control zones into the functional predictive map,i.e., predictive map 360, to produce predictive control zone map 265.One or both of predictive map 264 and predictive control zone map 265may be provided to control system 214, which generates control signalsto control one or more of the controllable subsystems 216, such aspropulsion subsystem 250 based upon the predictive map 264, predictivecontrol zone map 265, or both.

FIG. 5 is a flow diagram of an example of operation of predictive modelgenerator 210 and predictive map generator 212 in generating thepredictive model 350 and the functional predictive speed map 360. Atblock 362, predictive model generator 210 and predictive map generator212 receive an information map 258. The information map 258 may be anyof the maps 332, 333, 335, 337, 339, 341 or 343. In addition, block 361indicates that the received information map may be a single map. Block363 indicates that the information map may be multiple maps or multiplemap layers. Block 365 indicates that the information map 258 may takeother forms as well. At block 364, processing system 338 receives one ormore signals from machine speed sensor 146 or sensor 336 or both.

At block 372, processing system 338 processes the one or more receivedsignals to generate processed data 340 indicative of a machine speed ofagricultural harvester 100.

At block 382, predictive model generator 210 also obtains the geographiclocation 334 corresponding to the processed data. For instance, thepredictive model generator 210 can obtain the geographic position fromgeographic position sensor 204 and determine, based upon machine delays,machine speed, etc., a precise geographic location where the processeddata was taken or from which the processed data 340 was derived.

At block 384, predictive model generator 210 generates one or morepredictive models, such as predictive model 350, that model arelationship between a value in the one or more information map 258, anda machine speed being sensed by the in-situ sensor 208. VIvalue-to-speed model generator 342 generates a predictive model thatmodels a relationship between VI values in VI map 332 and machine speedsensed by in-situ sensor 208. Biomass-to-speed model generator 344generates a predictive model that models a relationship between biomassvalues in biomass map 335 and machine speed sensed by in-situ sensor208. Topography-to-speed model generator 345 generates a predictivemodel that models a relationship between one or more topography valuessuch as pitch, roll, or slope in topographic map 339 and machine speedsensed by in-situ sensor 208. Seeding characteristic-to-speed modelgenerator 346 generates a predictive model that models a relationshipbetween seeding characteristics in seeding map 343 and machine speedsensed by in-situ sensor 208. Yield-to-speed model generator 347generates a predictive model that models a relationship between yieldvalues in yield map 333 and machine speed sensed by in-situ sensor 208.Crop state-to-speed model generator 342 generates a predictive modelthat models a relationship between crop state values in crop state map337 and machine speed sensed by in-situ sensor 208. Soilproperty-to-speed model generator 351 generates a predictive model thatmodels a relationship between soil property values in soil property map341 and machine speed sensed by in-situ sensor 208. At block 386, thepredictive model 350 is provided to predictive map generator 212 whichgenerates a functional predictive speed map 360 that maps a predicted,target machine speed based on the information map 258 and the predictivespeed model 350. Speed map generator 352 may generate the functionalpredictive speed map 360 using a predictive model 350 that models arelationship between VI values in VI map 332 and machine speed and usingthe VI map 332. Speed map generator 352 may generate the functionalpredictive speed map 360 using a predictive model 350 that models arelationship between yield values in yield map 333 and machine speed andusing the yield map 333. Speed map generator 352 may generate thefunctional predictive speed map 360 using a predictive model 350 thatmodels a relationship between biomass values in biomass map 335 andmachine speed and using the biomass map 335. Speed map generator 352 maygenerate the functional predictive speed map 360 using a predictivemodel 350 that models a relationship between crop state values in cropstate map 337 and machine speed and using the crop state map 337. Speedmap generator 352 may generate the functional predictive speed map 360using a predictive model 350 that models a relationship betweentopographic values in topographic map 339 and machine speed and usingthe topographic map 332. Speed map generator 352 may generate thefunctional predictive speed map 360 using a predictive model 350 thatmodels a relationship between soil property values in soil property map341 and machine speed and using the soil property map 341. Speed mapgenerator 352 may generate the functional predictive speed map 360 usinga predictive model 350 that models a relationship between seedingcharacteristic values in seeding map 343 and machine speed and using theseeding map 343. Speed map generator 352 may generate the functionalpredictive speed map 360 using another predictive model 350 that modelsa relationship between other characteristic values on other map 353 andmachine speed and using the other map 353.

Thus, as an agricultural harvester is moving through a field performingan agricultural operation, one or more functional predictive speed maps360 are generated as the agricultural operation is being performed.

At block 394, predictive map generator 212 outputs the functionalpredictive speed map 360. At block 391 functional predictive speed mapgenerator 212 outputs the functional predictive speed map 360 forpresentation to and possible interaction by operator 260. At block 393,predictive map generator 212 may configure the map 360 for consumptionby control system 214. At block 395, predictive map generator 212 canalso provide the map 360 to control zone generator 213 for generation ofcontrol zones. At block 397, predictive map generator 212 configures themap 360 in other ways as well. The functional predictive speed map 360(with or without the control zones) is provided to control system 214.At block 396, control system 214 generates control signals to controlthe controllable subsystems 216 based upon the functional predictivespeed map 360. The control system 214 may control propulsion subsystem250 or other subsystems 399.

It can thus be seen that the present system takes an information mapthat maps an agricultural characteristic such as a vegetative index,crop state, seeding characteristic, soil properties, biomass, predictedyield, topography, or information from a prior operation pass or passesto different locations in a field. The present system also uses one ormore in-situ sensors that sense in-situ sensor data that is indicativeof a characteristic, indicative of machine speed, and generates a modelthat models a relationship between the characteristic sensed using thein-situ sensor, or a related characteristic, and the characteristicmapped in the information map. Thus, the present system generates afunctional predictive map using a model, in-situ data, and aninformation map and may configure the generated functional predictivemap for consumption by a control system, for presentation to a local orremote operator or other user, or both. For example, the control systemmay use the map to control one or more systems of a combine harvester.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. Theprocessors and servers are functional parts of the systems or devices towhich the processors and servers belong and are activated by andfacilitate the functionality of the other components or items in thosesystems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, the user actuatable operator interface mechanisms can beactuated using operator interface mechanisms such as a point and clickdevice, such as a track ball or mouse, hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc., a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the user actuatable operator interface mechanisms are displayed isa touch sensitive screen, the user actuatable operator interfacemechanisms can be actuated using touch gestures. Also, user actuatableoperator interface mechanisms can be actuated using speech commandsusing speech recognition functionality. Speech recognition may beimplemented using a speech detection device, such as a microphone, andsoftware that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic, and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, some of which are describedbelow, that perform the functions associated with those systems,components, logic, or interactions. In addition, any or all of thesystems, components, logic and interactions may be implemented bysoftware that is loaded into a memory and is subsequently executed by aprocessor or server or other computing component, as described below.Any or all of the systems, components, logic and interactions may alsobe implemented by different combinations of hardware, software,firmware, etc., some examples of which are described below. These aresome examples of different structures that may be used to implement anyor all of the systems, components, logic and interactions describedabove. Other structures may be used as well.

FIG. 6 is a block diagram of agricultural harvester 600, which may besimilar to agricultural harvester 100 shown in FIG. 2 . The agriculturalharvester 600 communicates with elements in a remote server architecture500. In some examples, remote server architecture 500 providescomputation, software, data access, and storage services that do notrequire end-user knowledge of the physical location or configuration ofthe system that delivers the services. In various examples, remoteservers may deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers maydeliver applications over a wide area network and may be accessiblethrough a web browser or any other computing component. Software orcomponents shown in FIG. 2 as well as data associated therewith, may bestored on servers at a remote location. The computing resources in aremote server environment may be consolidated at a remote data centerlocation, or the computing resources may be dispersed to a plurality ofremote data centers. Remote server infrastructures may deliver servicesthrough shared data centers, even though the services appear as a singlepoint of access for the user. Thus, the components and functionsdescribed herein may be provided from a remote server at a remotelocation using a remote server architecture. Alternatively, thecomponents and functions may be provided from a server, or thecomponents and functions can be installed on client devices directly, orin other ways.

In the example shown in FIG. 6 , some items are similar to those shownin FIG. 2 and those items are similarly numbered. FIG. 6 specificallyshows that predictive model generator 210 or predictive map generator212, or both, may be located at a server location 502 that is remotefrom the agricultural harvester 600. Therefore, in the example shown inFIG. 6 , agricultural harvester 600 accesses systems through remoteserver location 502.

FIG. 6 also depicts another example of a remote server architecture.FIG. 6 shows that some elements of FIG. 2 may be disposed at a remoteserver location 502 while others may be located elsewhere. By way ofexample, data store 202 may be disposed at a location separate fromlocation 502 and accessed via the remote server at location 502.Regardless of where the elements are located, the elements can beaccessed directly by agricultural harvester 600 through a network suchas a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users, or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated, or manual information collectionsystem. As the combine harvester 600 comes close to the machinecontaining the information collection system, such as a fuel truck priorto fueling, the information collection system collects the informationfrom the combine harvester 600 using any type of ad-hoc wirelessconnection. The collected information may then be forwarded to anothernetwork when the machine containing the received information reaches alocation where wireless telecommunication service coverage or otherwireless coverage—is available. For instance, a fuel truck may enter anarea having wireless communication coverage when traveling to a locationto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationmay be stored on the agricultural harvester 600 until the agriculturalharvester 600 enters an area having wireless communication coverage. Theagricultural harvester 600, itself, may send the information to anothernetwork.

It will also be noted that the elements of FIG. 2 , or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 500 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 7 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of agricultural harvester 100 for use ingenerating, processing, or displaying the maps discussed above. FIGS.8-9 are examples of handheld or mobile devices.

FIG. 7 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 2 , that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 8 shows one example in which device 16 is a tablet computer 600. InFIG. 8 , computer 600 is shown with user interface display screen 602.Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 600 may also usean on-screen virtual keyboard. Of course, computer 600 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 600 may also illustratively receive voice inputs as well.

FIG. 9 is similar to FIG. 8 except that the device is a smart phone 71.Smart phone 71 has a touch sensitive display 73 that displays icons ortiles or other user input mechanisms 75. Mechanisms 75 can be used by auser to run applications, make calls, perform data transfer operations,etc. In general, smart phone 71 is built on a mobile operating systemand offers more advanced computing capability and connectivity than afeature phone.

Note that other forms of the devices 16 are possible.

FIG. 10 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference to FIG. 10 , an example systemfor implementing some embodiments includes a computing device in theform of a computer 810 programmed to operate as discussed above.Components of computer 810 may include, but are not limited to, aprocessing unit 820 (which can comprise processors or servers fromprevious FIGS.), a system memory 830, and a system bus 821 that couplesvarious system components including the system memory to the processingunit 820. The system bus 821 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Memoryand programs described with respect to FIG. 2 can be deployed incorresponding portions of FIG. 10 .

Computer 810 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 831 and random access memory (RAM) 832. A basic input/outputsystem 833 (BIOS), containing the basic routines that help to transferinformation between elements within computer 810, such as duringstart-up, is typically stored in ROM 831. RAM 832 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 820. By way ofexample, and not limitation, FIG. 10 illustrates operating system 834,application programs 835, other program modules 836, and program data837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 10 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 10 , provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 10 , for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 10 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Example 1 is an agricultural work machine, comprising:

-   -   a communication system that receives an information map that        includes values of a first agricultural characteristic        corresponding to different geographic locations in a field;    -   a geographic position sensor that detects a geographic location        of the agricultural work machine;    -   an in-situ sensor that detects a value of a second agricultural        characteristic corresponding to the geographic location;    -   a predictive model generator that generates a predictive        agricultural model that models a relationship between the first        agricultural characteristic and the second agricultural        characteristic based on a value of the first agricultural        characteristic in the information map at the geographic location        and the value of the second agricultural characteristic detected        by the in-situ sensor at the geographic location; and    -   a predictive map generator that generates a functional        predictive machine speed map of the field, that maps predictive        machine speed values indicative of predicted speed of the        agricultural harvester at the different geographic locations in        the field, based on the values of the first agricultural        characteristic in the information map and based on the        predictive agricultural model.

Example 2 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator configures the functionalpredictive machine speed map for consumption by a control system thatgenerates control signals to control a subsystem on the agriculturalwork machine based on the predictive machine speed values on thefunctional predictive machine speed map.

Example 3 is the agricultural work machine of any or all previousexamples, wherein the in-situ sensor on the agricultural work machine isconfigured to detect, as the value of the second agriculturalcharacteristic, a value of a speed characteristic indicative of thespeed of the agricultural harvester corresponding to the geographiclocation.

Example 4 is the agricultural work machine of any or all previousexamples, further comprising a feed rate controller configured togenerate a feed rate control signal to control a controllable subsystemof the agricultural harvester based on a target feed rate of materialthrough the agricultural harvester and wherein the in-situ sensorcomprises:

a sensor configured to generate a sensor signal indicative of an outputof the feed rate controller; and

a processing system that receives the sensor signal and generatesprocessed data indicative of machine speed of the agricultural harvesterbased on the sensor signal.

Example 5 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a vegetative index mapof vegetative index (VI) values corresponding to different geographiclocations in a field, and wherein the predictive model generatorcomprises:

a VI-to-speed model generator that generates a predictive speed modelthat models a relationship between the VI values and the speedcharacteristic based on the VI value in the VI map at the geographiclocation and the value of the speed characteristic detected by thein-situ sensor at the geographic location.

Example 6 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a biomass map of biomassvalues corresponding to different geographic locations in a field, andwherein the predictive model generator comprises:

a biomass-to-speed model generator that generates a predictive speedmodel that models a relationship between the biomass values and thespeed characteristic based on the biomass value in the biomass map atthe geographic location and the value of the speed characteristicdetected by the in-situ sensor at the geographic location.

Example 7 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a topographic map oftopographic values corresponding to different geographic locations in afield, and wherein the predictive model generator comprises:

a topographic-to-speed model generator that generates a predictive speedmodel that models a relationship between the topographic values and thespeed characteristic based on the topographic value in the topographicmap at the geographic location and the value of the speed characteristicdetected by the in-situ sensor at the geographic location.

Example 8 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a predictive yield mapof predictive yield values corresponding to different geographiclocations in a field, and wherein the predictive model generatorcomprises:

a yield-to-speed model generator that generates a predictive speed modelthat models a relationship between the predictive yield values and thespeed characteristic based on the predictive yield value in thepredictive yield map at the geographic location and the value of thespeed characteristic detected by the in-situ sensor at the geographiclocation.

Example 9 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a soil properties map ofsoil property values corresponding to different geographic locations ina field, and wherein the predictive model generator comprises:

a soil property-to-speed model generator that generates a predictivespeed model that models a relationship between the soil property valuesand the speed characteristic based on the soil property value in thesoil property map at the geographic location and the value of the speedcharacteristic detected by the in-situ sensor at the geographiclocation.

Example 10 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a seeding characteristicmap of seeding characteristic values corresponding to differentgeographic locations in a field, and wherein the predictive modelgenerator comprises:

a seeding characteristic-to-speed model generator that generates apredictive speed model that models a relationship between the seedingcharacteristic values and the speed characteristic based on the seedingcharacteristic value in the seeding characteristic map at the geographiclocation and the value of the speed characteristic detected by thein-situ sensor at the geographic location.

Example 11 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a crop state map of cropstate values corresponding to different geographic locations in a field,and wherein the predictive model generator comprises:

a crop state-to-speed model generator that generates a predictive speedmodel that models a relationship between the crop state values and thespeed characteristic based on the crop state value in the crop state mapat the geographic location and the value of the speed characteristicdetected by the in-situ sensor at the geographic location.

Example 12 is a computer implemented method of generating a functionalpredictive agricultural map, comprising:

-   -   receiving an information map, at an agricultural work machine,        that indicates values of a first agricultural characteristic        corresponding to different geographic locations in a field;    -   detecting a geographic location of the agricultural work        machine;    -   detecting, with an in-situ sensor, a value of a second        agricultural characteristic corresponding to the geographic        location;    -   generating a predictive agricultural model that models a        relationship between the first agricultural characteristic and        the second agricultural characteristic; and    -   controlling a predictive map generator to generate a functional        predictive machine speed map of the field, that maps predictive        target machine speed values to the different locations in the        field based on the values of the first agricultural        characteristic in the information map and the predictive        agricultural model.

Example 13 is the computer implemented method of any or all previousexamples, and further comprising:

configuring the functional predictive machine speed map for a propulsioncontroller that generates control signals to control a controllablepropulsion subsystem on the agricultural work machine based on thefunctional predictive machine speed map.

Example 14 is the computer implemented method of any or all previousexamples, wherein detecting, with an in-situ sensor, a value of thesecond agricultural characteristic comprises detecting a speedcharacteristic corresponding to the geographic location, and whereinreceiving the information map comprises receiving one or more of avegetative index map, a biomass map, a crop state map, a soil propertymap, a predictive yield map, a topographic map and a seeding map.

Example 15 is the computer implemented method of any or all previousexamples, wherein receiving the information map comprises:

receiving a plurality of different information map layers, eachinformation map layer, of the plurality of different information maplayers indicating one or more of vegetative index values, biomassvalues, predictive yield values, crop state values, soil propertyvalues, seeding characteristic values and topographic values at thegeographic location.

Example 16 is the computer implemented method of any or all previousexamples, wherein receiving an information map comprises:

receiving an information map generated from a prior operation performedin the field.

Example 17 is the computer implemented method of any or all previousexamples, further comprising:

controlling an operator interface mechanism to present the functionalpredictive machine speed map.

Example 18 is an agricultural work machine, comprising:

-   -   a communication system that receives an information map that        indicates values of a first agricultural characteristic        corresponding to different geographic locations in a field;    -   a geographic position sensor that detects a geographic location        of the agricultural work machine;    -   an in-situ sensor that detects a value of a speed        characteristic, indicative of a speed of the agricultural work        machine corresponding to the geographic location;    -   a predictive model generator that generates a predictive speed        model that models a relationship between the first agricultural        characteristic and the speed characteristic based on the value        of the first agricultural characteristic in the information map        at the geographic location and the value of the speed        characteristic detected by the in-situ sensor at the geographic        location; and    -   a predictive map generator that generates a functional        predictive speed map of the field, that maps predictive speed        characteristic values, indicative of target machine speeds, to        the different locations in the field, based on the values of the        first agricultural characteristic in the information map and        based on the predictive speed model.

Example 19 is the agricultural work machine of any or all previousexamples, wherein the in-situ sensor comprises:

a machine speed sensor configured to detect the speed of theagricultural work machine.

Example 20 is the agricultural work machine of any or all previousexamples wherein the predictive map generator is configured to configurethe functional predictive speed map for consumption by a control systemto control a propulsion subsystem based on the target machine speeds ofthe functional predictive speed map.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of the claims.

What is claimed is:
 1. An agricultural system comprising: acommunication system that receives an information map that includesvalues of a first agricultural characteristic corresponding to differentgeographic locations in a field; a geographic position sensor thatdetects a geographic location of an agricultural work machine; anin-situ sensor that detects a value of a second agriculturalcharacteristic corresponding to a geographic location; one or moreprocessors; and a data store that stores computer executableinstructions that when executed by the one or more processors, configurethe one or more processors to: generate a predictive agricultural modelthat models a relationship between the first agricultural characteristicand the second agricultural characteristic based on a value of the firstagricultural characteristic in the information map corresponding to thegeographic location and the value of the second agriculturalcharacteristic detected by the in-situ sensor corresponding to thegeographic location; and generate a functional predictive machine speedmap of the field, that maps predictive machine speed values indicativeof predicted travel speed of the agricultural work machine at thedifferent geographic locations in the field, based on the values of thefirst agricultural characteristic in the information map and based onthe predictive agricultural model.
 2. The agricultural system of claim1, wherein the computer executable instructions, when executed by theone or more processors, further configure the one or more processors to:generate control signals to control a propulsion subsystem on theagricultural work machine based on the predictive machine speed valuesin the functional predictive machine speed map.
 3. The agriculturalsystem of claim 1, wherein the in-situ sensor on the agricultural workmachine is configured to detect, as the value of the second agriculturalcharacteristic, a value of a speed characteristic indicative of thetravel speed of the agricultural work machine corresponding to thegeographic location.
 4. The agricultural system of claim 3, wherein thecomputer executable instructions, when executed by the one or moreprocessors, further configure the one or more processors to generate afeed rate control signal to control a controllable subsystem of theagricultural work machine based on a target feed rate of materialthrough the agricultural work machine and wherein the in-situ sensorcomprises: a sensor configured to generate a sensor signal indicative ofan output of the feed rate controller; and a processing system thatreceives and processes the sensor signal to generate the value of thespeed characteristic indicative of the travel speed of the agriculturalwork machine corresponding to the geographic location.
 5. Theagricultural system of claim 3, wherein the information map comprises avegetative index map that includes, as the values of the firstagricultural characteristic, vegetative index (VI) values correspondingto the different geographic locations in the field, and wherein thepredictive agricultural model comprises: a predictive speed model thatmodels a relationship between VI and the speed characteristic based onthe VI value in the VI map corresponding to the geographic location andthe value of the speed characteristic detected by the in-situ sensorcorresponding to the geographic location.
 6. The agricultural system ofclaim 3, wherein the information map comprises a biomass map thatincludes, as tile values of the first agricultural characteristic,biomass values corresponding to the different geographic locations inthe field, and wherein the predictive agricultural model comprises: apredictive speed model that models a relationship between biomass andthe speed characteristic based on the biomass value in the biomass mapcorresponding to the geographic location and the value of the speedcharacteristic detected by the in-situ sensor corresponding to thegeographic location.
 7. The as system or claim 3, wherein theinformation map comprises a topographic map that includes, as the valuesof the first agricultural characteristic, values of a topographiccharacteristic corresponding to different geographic locations in thefield, and wherein the predictive agricultural model comprises: apredictive speed model that models a relationship between thetopographic characteristic and the speed characteristic based on thevalue of the topographic characteristic in the topographic mapcorresponding to the geographic location and the value of the speedcharacteristic detected by the in-situ sensor corresponding to thegeographic location.
 8. The agricultural system of claim 3, wherein theinformation map comprises a predictive yield map that includes, as thevalues of the first agricultural characteristic, predictive yield valuescorresponding to the different geographic locations in the field, andwherein the predictive agricultural model comprises: a predictive speedmodel that models a relationship between yield and the speedcharacteristic based on the predictive yield value in the predictiveyield map corresponding to the geographic location and the value of thespeed characteristic detected by the in-situ sensor corresponding to thegeographic location.
 9. The agricultural system of claim 3, wherein theinformation map comprises a soil property map that includes, as thevalues of the first agricultural characteristic, values of a soilproperty corresponding to the different geographic locations in thefield, and wherein the predictive agricultural model comprises: apredictive speed model that models a relationship between the soilproperty and the speed characteristic based on the value of the soilproperty in the soil property map corresponding to the geographiclocation and the value of the speed characteristic detected by thein-situ sensor corresponding to the geographic location.
 10. Theagricultural stem of claim 3, wherein the information map comprises aseeding characteristic map that includes, as the values of the firstagricultural characteristic, values of a seeding characteristiccorresponding to the different geographic locations in the field, andwherein the predictive agricultural model comprises: a predictive speedmodel that models a relationship between the seeding characteristic andthe speed characteristic based on the value of the seedingcharacteristic in the seeding characteristic map corresponding to thegeographic location and a value of the speed characteristic detected bythe in-situ sensor corresponding to the geographic location.
 11. Theagricultural system of claim 3, wherein the information map comprises acrop state map that includes, as values of the first agriculturalcharacteristic, crop state values corresponding to the differentgeographic locations in the field, and wherein the predictiveagricultural model comprises: a predictive speed model that models arelationship between crop state and die speed characteristic based onthe crop state value in the crop state map corresponding to thegeographic location and the value of the speed characteristic detectedby the in-situ sensor corresponding to the geographic location.
 12. Acomputer implemented method of generating a functional predictiveagricultural map, comprising: receiving an information map thatindicates values of a first agricultural characteristic corresponding todifferent geographic locations in a field; detecting a geographiclocation of an agricultural work machine; detecting, with an in-situsensor, a value of a second agricultural characteristic corresponding tothe geographic location; generating a predictive agricultural model thatmodels a relationship between the first agricultural characteristic andthe second agricultural characteristic; and controlling a predictive mapgenerator to generate, as the functional predictive agricultural map, afunctional predictive machine speed map of the field, that mapspredictive machine travel speed values to the different locations in thefield based on the values of the first agricultural characteristic inthe information map and the predictive agricultural model.
 13. Thecomputer implemented method of claim 12, and further comprising:configuring the functional predictive machine speed map for a propulsioncontroller that generates control signals to control a controllablepropulsion subsystem on the agricultural work machine based on thefunctional predictive machine speed map.
 14. The computer implementedmethod of claim 12, wherein detecting, with the in-situ sensor, thevalue of the second agricultural characteristic comprises detecting aspeed characteristic value corresponding to the geographic location, andwherein receiving the information map comprises receiving one or more ofa vegetative index map, a biomass map, a crop state map, a soil propertymap, a predictive yield map, a topographic map, and a seeding map. 15.The computer implemented method of claim 14, wherein receiving theinformation map comprises: receiving an information map generated from aprior operation performed in the field.
 16. The computer implementedmethod of claim 12, where receiving the information map comprises:receiving a plurality of different information map layers, eachinformation map layer, of the plurality of different information maplayers, indicating one or more of vegetative index values, biomassvalues, predictive yield values, crop state values, soil propertyvalues, seeding characteristic values, and topographic valuescorresponding to the different geographic locations.
 17. The computerimplemented method of claim 12, further comprising: controlling anoperator interface mechanism to present the functional predictivemachine speed map.
 18. An agricultural system comprising: acommunication system that receives an information map that indicatesvalues of an agricultural characteristic corresponding to differentgeographic locations in a field; a geographic position sensor thatdetects a geographic location of an agricultural work machine; anin-situ sensor that detects a speed characteristic value, of a speedcharacteristic, indicative of a travel speed of the agricultural workmachine corresponding to the geographic location; one or moreprocessors; and a data store that stores computer executableinstructions that, when executed by the one or more processors,configure the one Or more processors to: generate a predictive speedmodel that models a relationship between the first agriculturalcharacteristic and the speed characteristic based on the value of thefirst agricultural characteristic in the information map correspondingto the geographic location and the speed characteristic value of thespeed characteristic detected by the in-situ sensor corresponding to thegeographic location; and generate a functional predictive speed map ofthe field, that maps predictive speed characteristic values, indicativeof predictive travel speeds of the agricultural work machine, to thedifferent locations in the field, based on the values of theagricultural characteristic in the information map and based on thepredictive speed model.
 19. The agricultural system of claim 18, whereinthe in-situ sensor comprises: a machine speed sensor configured todetect the travel speed of the agricultural work machine.
 20. Theagricultural system of claim 19, wherein the computer executableinstructions, when executed by the one or more processors, furtherconfigure the one or more processors to; generate control signals tocontrol a propulsion subsystem based on the predictive speedcharacteristic values in the functional predictive speed map.